package com.flink.timewindow.watermark;

import org.apache.flink.api.common.eventtime.SerializableTimestampAssigner;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.functions.JoinFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;

public class WindowJoinDemo {

    public static void main(String[] args) throws Exception {

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        env.setParallelism(1);

        SingleOutputStreamOperator<Tuple2<String, Integer>> ds1 = env
                .fromElements(//数据源
                        Tuple2.of("a", 1),
                        Tuple2.of("a", 2),
                        Tuple2.of("b", 3),
                        Tuple2.of("c", 4),
                        Tuple2.of("c",5)
                )
                .assignTimestampsAndWatermarks(
                        WatermarkStrategy
                                .<Tuple2<String, Integer>>forMonotonousTimestamps()
                                //指定时间戳分配器，从数据中提取
                                .withTimestampAssigner(new SerializableTimestampAssigner<Tuple2<String, Integer>>() {
                                    @Override
                                    public long extractTimestamp(Tuple2<String, Integer> element, long recordTimestamp) {
                                        return element.f1 * 1000L;
                                    }
                                })
                );

        SingleOutputStreamOperator<Tuple3<String, Integer, Integer>> ds2 = env
                .fromElements(
                        Tuple3.of("a", 1, 1),
                        Tuple3.of("a", 8, 1),
                        Tuple3.of("a", 11, 1),
                        Tuple3.of("b", 2, 1),
                        Tuple3.of("b", 12, 1),
                        Tuple3.of("c", 1, 1),
                        Tuple3.of("c", 13, 1)
                )
                .assignTimestampsAndWatermarks(
                        WatermarkStrategy
                                .<Tuple3<String, Integer, Integer>>forMonotonousTimestamps()
                                .withTimestampAssigner(
                                        (e, ts) -> e.f1 * 1000
                                )
                );

        // TODO window join
        // 1. 落在同一个时间窗口范围内才能匹配
        // 2. 根据keyBy的key，来进行匹配关联
        // 3. 只能拿到匹配上的数据，类似有固定时间范围的inner join
        DataStream<String> join = ds1.join(ds2)
                .where(value1 -> value1.f0)//ds1的keyBy
                .equalTo(value2 -> value2.f0)//ds2的keyBy
                .window(TumblingEventTimeWindows.of(Time.seconds(5)))//开窗，窗口大小5s: [0,5)、[5,10)、[10,15)、.....
                .apply(
                        new JoinFunction<Tuple2<String, Integer>, Tuple3<String, Integer, Integer>, String>() {
                            //关联上的数据，调用join方法
                            //first ds1的数据   ds2的数据
                            @Override
                            public String join(Tuple2<String, Integer> first, Tuple3<String, Integer, Integer> second) throws Exception {

                                return first + "<-->" + second;
                            }
                        }
                );
        join.print();


        env.execute();
    }
}
